class_docstr: Pipeline for text-to-image and 3D generation using LDM3D.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
      - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
      - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` to denoise the encoded image latents.
      low_res_scheduler ([`SchedulerMixin`]):
          A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of
          [`DDPMScheduler`].
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offensive or harmful.
          Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
          about a model's potential harms.
      feature_extractor ([`~transformers.CLIPImageProcessor`]):
          A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
docstr: The call function to the pipeline for generation.
  
  Args:
      prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
      image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
          `Image` or tensor representing an image batch to be upscaled.
      num_inference_steps (`int`, *optional*, defaults to 50):
          The number of denoising steps. More denoising steps usually lead to a higher quality image at the
          expense of slower inference.
      guidance_scale (`float`, *optional*, defaults to 5.0):
          A higher guidance scale value encourages the model to generate images closely linked to the text
          `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
      negative_prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts to guide what to not include in image generation. If not defined, you need to
          pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
      num_images_per_prompt (`int`, *optional*, defaults to 1):
          The number of images to generate per prompt.
      eta (`float`, *optional*, defaults to 0.0):
          Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
          to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
      generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
          A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
          generation deterministic.
      latents (`torch.FloatTensor`, *optional*):
          Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
          generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
          tensor is generated by sampling using the supplied random `generator`.
      prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
          provided, text embeddings are generated from the `prompt` input argument.
      negative_prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
          not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
      output_type (`str`, *optional*, defaults to `"pil"`):
          The output format of the generated image. Choose between `PIL.Image` or `np.array`.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
          plain tuple.
      callback (`Callable`, *optional*):
          A function that calls every `callback_steps` steps during inference. The function is called with the
          following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
      callback_steps (`int`, *optional*, defaults to 1):
          The frequency at which the `callback` function is called. If not specified, the callback is called at
          every step.
      cross_attention_kwargs (`dict`, *optional*):
          A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
          [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
  
  Examples:
  
  Returns:
      [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
          If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
          otherwise a `tuple` is returned where the first element is a list with the generated images and the
          second element is a list of `bool`s indicating whether the corresponding generated image contains
          "not-safe-for-work" (nsfw) content.
docstr_tok: ["The","call","function","to","the","pipeline","for","generation",".","Args",":","prompt","(","`","str","`","or","`","List","[","str","]","`",",","*","optional","*",")",":","The","prompt","or","prompts","to","guide","image","generation",".","If","not","defined",",","you","need","to","pass","`","prompt_embeds","`",".","image","(","`","torch.FloatTensor","`",",","`","PIL.Image.Image","`",",","`","np.ndarray","`",",","`","List","[","torch.FloatTensor","]","`",",","`","List","[","PIL.Image.Image","]","`",",","or","`","List","[","np.ndarray","]","`",")",":","`","Image","`","or","tensor","representing","an","image","batch","to","be","upscaled",".","num_inference_steps","(","`","int","`",",","*","optional","*",",","defaults","to","50",")",":","The","number","of","denoising","steps",".","More","denoising","steps","usually","lead","to","a","higher","quality","image","at","the","expense","of","slower","inference",".","guidance_scale","(","`","float","`",",","*","optional","*",",","defaults","to","5.0",")",":","A","higher","guidance","scale","value","encourages","the","model","to","generate","images","closely","linked","to","the","text","`","prompt","`","at","the","expense","of","lower","image","quality",".","Guidance","scale","is","enabled","when","`","guidance_scale",">","1","`",".","negative_prompt","(","`","str","`","or","`","List","[","str","]","`",",","*","optional","*",")",":","The","prompt","or","prompts","to","guide","what","to","not","include","in","image","generation",".","If","not","defined",",","you","need","to","pass","`","negative_prompt_embeds","`","instead",".","Ignored","when","not","using","guidance","(","`","guidance_scale","<","1","`",")",".","num_images_per_prompt","(","`","int","`",",","*","optional","*",",","defaults","to","1",")",":","The","number","of","images","to","generate","per","prompt",".","eta","(","`","float","`",",","*","optional","*",",","defaults","to","0.0",")",":","Corresponds","to","parameter","eta","(","\u03b7",")","from","the","[","DDIM","]","(","https",":","\/\/arxiv.org\/abs\/2010.02502",")","paper",".","Only","applies","to","the","[","`","~schedulers.DDIMScheduler","`","]",",","and","is","ignored","in","other","schedulers",".","generator","(","`","torch.Generator","`","or","`","List","[","torch.Generator","]","`",",","*","optional","*",")",":","A","[","`","torch.Generator","`","]","(","https",":","\/\/pytorch.org\/docs\/stable\/generated\/torch.Generator.html",")","to","make","generation","deterministic",".","latents","(","`","torch.FloatTensor","`",",","*","optional","*",")",":","Pre-generated","noisy","latents","sampled","from","a","Gaussian","distribution",",","to","be","used","as","inputs","for","image","generation",".","Can","be","used","to","tweak","the","same","generation","with","different","prompts",".","If","not","provided",",","a","latents","tensor","is","generated","by","sampling","using","the","supplied","random","`","generator","`",".","prompt_embeds","(","`","torch.FloatTensor","`",",","*","optional","*",")",":","Pre-generated","text","embeddings",".","Can","be","used","to","easily","tweak","text","inputs","(","prompt","weighting",")",".","If","not","provided",",","text","embeddings","are","generated","from","the","`","prompt","`","input","argument",".","negative_prompt_embeds","(","`","torch.FloatTensor","`",",","*","optional","*",")",":","Pre-generated","negative","text","embeddings",".","Can","be","used","to","easily","tweak","text","inputs","(","prompt","weighting",")",".","If","not","provided",",","`","negative_prompt_embeds","`","are","generated","from","the","`","negative_prompt","`","input","argument",".","output_type","(","`","str","`",",","*","optional","*",",","defaults","to","`","``","pil","''","`",")",":","The","output","format","of","the","generated","image",".","Choose","between","`","PIL.Image","`","or","`","np.array","`",".","return_dict","(","`","bool","`",",","*","optional","*",",","defaults","to","`","True","`",")",":","Whether","or","not","to","return","a","[","`","~pipelines.stable_diffusion.StableDiffusionPipelineOutput","`","]","instead","of","a","plain","tuple",".","callback","(","`","Callable","`",",","*","optional","*",")",":","A","function","that","calls","every","`","callback_steps","`","steps","during","inference",".","The","function","is","called","with","the","following","arguments",":","`","callback","(","step",":","int",",","timestep",":","int",",","latents",":","torch.FloatTensor",")","`",".","callback_steps","(","`","int","`",",","*","optional","*",",","defaults","to","1",")",":","The","frequency","at","which","the","`","callback","`","function","is","called",".","If","not","specified",",","the","callback","is","called","at","every","step",".","cross_attention_kwargs","(","`","dict","`",",","*","optional","*",")",":","A","kwargs","dictionary","that","if","specified","is","passed","along","to","the","[","`","AttentionProcessor","`","]","as","defined","in","[","`","self.processor","`","]","(","https",":","\/\/github.com\/huggingface\/diffusers\/blob\/main\/src\/diffusers\/models\/attention_processor.py",")",".","Examples",":","Returns",":","[","`","~pipelines.stable_diffusion.StableDiffusionPipelineOutput","`","]","or","`","tuple","`",":","If","`","return_dict","`","is","`","True","`",",","[","`","~pipelines.stable_diffusion.StableDiffusionPipelineOutput","`","]","is","returned",",","otherwise","a","`","tuple","`","is","returned","where","the","first","element","is","a","list","with","the","generated","images","and","the","second","element","is","a","list","of","`","bool","`","s","indicating","whether","the","corresponding","generated","image","contains","``","not-safe-for-work","''","(","nsfw",")","content","."]
code: def __call__(
      self,
      prompt: Union[str, List[str]] = None,
      rgb: PipelineImageInput = None,
      depth: PipelineDepthInput = None,
      num_inference_steps: int = 75,
      guidance_scale: float = 9.0,
      noise_level: int = 20,
      negative_prompt: Optional[Union[str, List[str]]] = None,
      num_images_per_prompt: Optional[int] = 1,
      eta: float = 0.0,
      generator: Optional[
          Union[torch.Generator, List[torch.Generator]]
      ] = None,
      latents: Optional[torch.FloatTensor] = None,
      prompt_embeds: Optional[torch.FloatTensor] = None,
      negative_prompt_embeds: Optional[torch.FloatTensor] = None,
      output_type: Optional[str] = "pil",
      return_dict: bool = True,
      callback: Optional[
          Callable[[int, int, torch.FloatTensor], None]
      ] = None,
      callback_steps: int = 1,
      cross_attention_kwargs: Optional[Dict[str, Any]] = None,
      target_res: Optional[List[int]] = [1024, 1024],
  ):
      r"""
      The call function to the pipeline for generation.
  
      Args:
          prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
          image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
              `Image` or tensor representing an image batch to be upscaled.
          num_inference_steps (`int`, *optional*, defaults to 50):
              The number of denoising steps. More denoising steps usually lead to a higher quality image at the
              expense of slower inference.
          guidance_scale (`float`, *optional*, defaults to 5.0):
              A higher guidance scale value encourages the model to generate images closely linked to the text
              `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
          negative_prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts to guide what to not include in image generation. If not defined, you need to
              pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
          num_images_per_prompt (`int`, *optional*, defaults to 1):
              The number of images to generate per prompt.
          eta (`float`, *optional*, defaults to 0.0):
              Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
              to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
          generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
              A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
              generation deterministic.
          latents (`torch.FloatTensor`, *optional*):
              Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
              generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
              tensor is generated by sampling using the supplied random `generator`.
          prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
              provided, text embeddings are generated from the `prompt` input argument.
          negative_prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
              not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
          output_type (`str`, *optional*, defaults to `"pil"`):
              The output format of the generated image. Choose between `PIL.Image` or `np.array`.
          return_dict (`bool`, *optional*, defaults to `True`):
              Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
              plain tuple.
          callback (`Callable`, *optional*):
              A function that calls every `callback_steps` steps during inference. The function is called with the
              following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
          callback_steps (`int`, *optional*, defaults to 1):
              The frequency at which the `callback` function is called. If not specified, the callback is called at
              every step.
          cross_attention_kwargs (`dict`, *optional*):
              A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
              [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
  
      Examples:
  
      Returns:
          [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
              If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
              otherwise a `tuple` is returned where the first element is a list with the generated images and the
              second element is a list of `bool`s indicating whether the corresponding generated image contains
              "not-safe-for-work" (nsfw) content.
      """
      # 1. Check inputs. Raise error if not correct
      self.check_inputs(
          prompt,
          rgb,
          noise_level,
          callback_steps,
          negative_prompt,
          prompt_embeds,
          negative_prompt_embeds,
      )
      # 2. Define call parameters
      if prompt is not None and isinstance(prompt, str):
          batch_size = 1
      elif prompt is not None and isinstance(prompt, list):
          batch_size = len(prompt)
      else:
          batch_size = prompt_embeds.shape[0]
  
      device = self._execution_device
      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      do_classifier_free_guidance = guidance_scale > 1.0
  
      # 3. Encode input prompt
      prompt_embeds, negative_prompt_embeds = self.encode_prompt(
          prompt,
          device,
          num_images_per_prompt,
          do_classifier_free_guidance,
          negative_prompt,
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
      )
      # For classifier free guidance, we need to do two forward passes.
      # Here we concatenate the unconditional and text embeddings into a single batch
      # to avoid doing two forward passes
      if do_classifier_free_guidance:
          prompt_embeds = torch.cat(
              [negative_prompt_embeds, prompt_embeds]
          )
  
      # 4. Preprocess image
      rgb, depth = self.image_processor.preprocess(
          rgb, depth, target_res=target_res
      )
      rgb = rgb.to(dtype=prompt_embeds.dtype, device=device)
      depth = depth.to(dtype=prompt_embeds.dtype, device=device)
  
      # 5. set timesteps
      self.scheduler.set_timesteps(num_inference_steps, device=device)
      timesteps = self.scheduler.timesteps
  
      # 6. Encode low resolutiom image to latent space
      image = torch.cat([rgb, depth], axis=1)
      latent_space_image = self.vae.encode(image).latent_dist.sample(
          generator
      )
      latent_space_image *= self.vae.scaling_factor
      noise_level = torch.tensor(
          [noise_level], dtype=torch.long, device=device
      )
      # noise_rgb = randn_tensor(rgb.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
      # rgb = self.low_res_scheduler.add_noise(rgb, noise_rgb, noise_level)
      # noise_depth = randn_tensor(depth.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
      # depth = self.low_res_scheduler.add_noise(depth, noise_depth, noise_level)
  
      batch_multiplier = 2 if do_classifier_free_guidance else 1
      latent_space_image = torch.cat(
          [latent_space_image]
          * batch_multiplier
          * num_images_per_prompt
      )
      noise_level = torch.cat(
          [noise_level] * latent_space_image.shape[0]
      )
  
      # 7. Prepare latent variables
      height, width = latent_space_image.shape[2:]
      num_channels_latents = self.vae.config.latent_channels
  
      latents = self.prepare_latents(
          batch_size * num_images_per_prompt,
          num_channels_latents,
          height,
          width,
          prompt_embeds.dtype,
          device,
          generator,
          latents,
      )
  
      # 8. Check that sizes of image and latents match
      num_channels_image = latent_space_image.shape[1]
      if (
          num_channels_latents + num_channels_image
          != self.unet.config.in_channels
      ):
          raise ValueError(
              f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
              f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
              f" `num_channels_image`: {num_channels_image} "
              f" = {num_channels_latents+num_channels_image}. Please verify the config of"
              " `pipeline.unet` or your `image` input."
          )
  
      # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
  
      # 10. Denoising loop
      num_warmup_steps = (
          len(timesteps) - num_inference_steps * self.scheduler.order
      )
      with self.progress_bar(total=num_inference_steps) as progress_bar:
          for i, t in enumerate(timesteps):
              # expand the latents if we are doing classifier free guidance
              latent_model_input = (
                  torch.cat([latents] * 2)
                  if do_classifier_free_guidance
                  else latents
              )
  
              # concat latents, mask, masked_image_latents in the channel dimension
              latent_model_input = self.scheduler.scale_model_input(
                  latent_model_input, t
              )
              latent_model_input = torch.cat(
                  [latent_model_input, latent_space_image], dim=1
              )
  
              # predict the noise residual
              noise_pred = self.unet(
                  latent_model_input,
                  t,
                  encoder_hidden_states=prompt_embeds,
                  cross_attention_kwargs=cross_attention_kwargs,
                  class_labels=noise_level,
                  return_dict=False,
              )[0]
  
              # perform guidance
              if do_classifier_free_guidance:
                  noise_pred_uncond, noise_pred_text = noise_pred.chunk(
                      2
                  )
                  noise_pred = noise_pred_uncond + guidance_scale * (
                      noise_pred_text - noise_pred_uncond
                  )
  
              # compute the previous noisy sample x_t -> x_t-1
              latents = self.scheduler.step(
                  noise_pred,
                  t,
                  latents,
                  **extra_step_kwargs,
                  return_dict=False,
              )[0]
  
              # call the callback, if provided
              if i == len(timesteps) - 1 or (
                  (i + 1) > num_warmup_steps
                  and (i + 1) % self.scheduler.order == 0
              ):
                  progress_bar.update()
                  if callback is not None and i % callback_steps == 0:
                      callback(i, t, latents)
  
      if not output_type == "latent":
          # make sure the VAE is in float32 mode, as it overflows in float16
          needs_upcasting = (
              self.vae.dtype == torch.float16
              and self.vae.config.force_upcast
          )
  
          if needs_upcasting:
              self.upcast_vae()
              latents = latents.to(
                  next(
                      iter(self.vae.post_quant_conv.parameters())
                  ).dtype
              )
  
          image = self.vae.decode(
              latents / self.vae.scaling_factor, return_dict=False
          )[0]
  
          # cast back to fp16 if needed
          if needs_upcasting:
              self.vae.to(dtype=torch.float16)
  
          image, has_nsfw_concept = self.run_safety_checker(
              image, device, prompt_embeds.dtype
          )
  
      else:
          image = latents
          has_nsfw_concept = None
  
      if has_nsfw_concept is None:
          do_denormalize = [True] * image.shape[0]
      else:
          do_denormalize = [
              not has_nsfw for has_nsfw in has_nsfw_concept
          ]
  
      rgb, depth = self.image_processor.postprocess(
          image, output_type=output_type, do_denormalize=do_denormalize
      )
  
      # 11. Apply watermark
      if output_type == "pil" and self.watermarker is not None:
          rgb = self.watermarker.apply_watermark(rgb)
  
      # Offload last model to CPU
      if (
          hasattr(self, "final_offload_hook")
          and self.final_offload_hook is not None
      ):
          self.final_offload_hook.offload()
  
      if not return_dict:
          return ((rgb, depth), has_nsfw_concept)
  
      return LDM3DPipelineOutput(
          rgb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept
      )
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  from diffusers.image_processor import PipelineDepthInput, PipelineImageInput, VaeImageProcessorLDM3D
  from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
  from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d import LDM3DPipelineOutput
  from diffusers.schedulers import DDPMScheduler, KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 8 for calling the StableDiffusionUpscaleLDM3DPipeline obj's underlying member method code with example usage: obj.__call__(prompt, rgb, depth, num_inference_steps, guidance_scale, noise_level, negative_prompt, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, output_type, return_dict, callback, callback_steps, cross_attention_kwargs, target_res) and returns: LDM3DPipelineOutput, rgb,  depth,  has_nsfw_concept